2023
DOI: 10.1038/s41524-022-00955-9
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Machine learning-driven synthesis of TiZrNbHfTaC5 high-entropy carbide

Abstract: Synthesis of high-entropy carbides (HEC) requires high temperatures that can be provided by electric arc plasma method. However, the formation temperature of a single-phase sample remains unknown. Moreover, under some temperatures multi-phase structures can emerge. In this work, we developed an approach for a controllable synthesis of HEC TiZrNbHfTaC5 based on theoretical and experimental techniques. We used Canonical Monte Carlo (CMC) simulations with the machine learning interatomic potentials to determine t… Show more

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Cited by 19 publications
(11 citation statements)
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“…Four-and five-element high entropy ceramics exhibited enhanced thermal [5,6] and mechanical [7,8] properties, in addition to phase structure stability [9], in comparison with traditional high temperature ceramics (UHTC). Identical effects of characteristic enhancement were reported for highentropy boride [5,6], carbide [10], nitride [7,8] and oxide [11] structures. Oxide-based high entropy ceramics (HEO) is one of the most promising materials among the variety of high-entropy ceramics [12], especially for ZrO 2 -and HfO 2 -based protective HEOs [13].…”
Section: Introductionsupporting
confidence: 63%
“…Four-and five-element high entropy ceramics exhibited enhanced thermal [5,6] and mechanical [7,8] properties, in addition to phase structure stability [9], in comparison with traditional high temperature ceramics (UHTC). Identical effects of characteristic enhancement were reported for highentropy boride [5,6], carbide [10], nitride [7,8] and oxide [11] structures. Oxide-based high entropy ceramics (HEO) is one of the most promising materials among the variety of high-entropy ceramics [12], especially for ZrO 2 -and HfO 2 -based protective HEOs [13].…”
Section: Introductionsupporting
confidence: 63%
“…The metal formulation can be engineered to contain multiple atoms, and when there are five or more metals, the configurational entropy stabilizes single-phase high entropy carbides (HECs) [40,55,56] and their thin films [41]. Recently, ML-IAPs have been developed for various HECs including a deep learning potential for (ZrHfTiNbTa)C 5 [57] and a low rank potential for (TiZrNbHfTa)C 5 [58]. Herein, we illustrate that data found within AFLOW, coupled with active learning, can be used to train reliable MTPs for binary carbides, with the future goal of HECs in mind.…”
Section: Resultsmentioning
confidence: 99%
“…In this study, we employed vacuum-free electric arc synthesis of powder materials. 23,24,26 The laboratory set-up used is a DC plasma-chemical electric arc reactor (Fig. 1).…”
Section: Methodsmentioning
confidence: 99%